Papers
Topics
Authors
Recent
Search
2000 character limit reached

Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint

Published 4 Feb 2024 in cs.AI, cs.IR, and cs.LG | (2402.03388v2)

Abstract: Users' behavioral footprints online enable firms to discover behavior-based user segments (or, segments) and deliver segment specific messages to users. Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure). Even high quality discovery becomes futile when delivery fails. Many sophisticated algorithms exist for discovering behavioral segments; however, these ignore the delivery component. The problem is compounded because (i) the discovery is performed on the behavior data space in firms' data (e.g., user clicks), while the delivery is predicated on the static data space (e.g., geo, age) as defined by media; and (ii) firms work under budget constraint. We introduce a stochastic optimization based algorithm for delivery optimized discovery of behavioral user segmentation and offer new metrics to address the joint optimization. We leverage optimization under a budget constraint for delivery combined with a learning-based component for discovery. Extensive experiments on a public dataset from Google and a proprietary dataset show the effectiveness of our approach by simultaneously improving delivery metrics, reducing budget spend and achieving strong predictive performance in discovery.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. 2017. https://www.kaggle.com/bigquery/google-analytics-sample
  2. 2022a. https://www.statista.com/statistics/276671/global-internet-advertising-expenditure-by-type/
  3. 2022. https://www.facebook.com/help/274400362581037
  4. 2022. https://advertising.amazon.com/library/guides/media-buying
  5. 2022b. https://www.statista.com/statistics/1106617/number-of-pages-seen-buying-session
  6. 2023. https://www.investopedia.com/terms/m/marketsegmentation.asp
  7. 2023. https://blog.hubspot.com/marketing/behavioral-segmentation
  8. Budget pacing for targeted online advertisements at linkedin. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1613–1619.
  9. Time-series clustering - A decade review. Inf. Syst. 53 (2015), 16–38. https://doi.org/10.1016/j.is.2015.04.007
  10. Clustering with deep learning: Taxonomy and new methods. arXiv preprint arXiv:1801.07648 (2018).
  11. A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. Journal of Big Data 7, 1 (2020), 1–23.
  12. Budget management strategies in repeated auctions. In Proceedings of the 26th International Conference on World Wide Web. 15–23.
  13. Dual mirror descent for online allocation problems. In International Conference on Machine Learning. PMLR, 613–628.
  14. Convex optimization. Cambridge university press.
  15. Purtreeclust: A clustering algorithm for customer segmentation from massive customer transaction data. IEEE Transactions on Knowledge and Data Engineering 30, 3 (2017), 559–572.
  16. Multiplicative pacing equilibria in auction markets. Operations Research 70, 2 (2022), 963–989.
  17. Application of K-Means algorithm for efficient customer segmentation: a strategy for targeted customer services. (2015).
  18. Budget optimization in search-based advertising auctions. In Proceedings of the 8th ACM conference on Electronic commerce. 40–49.
  19. A survey on Optimisation-based Semi-supervised Clustering Methods. In 2021 IEEE International Conference on Big Knowledge (ICBK). IEEE, 477–482.
  20. Sunil Gupta. 2014. Marketing reading: Segmentation and targeting. Core Curriculum Readings Series. Boston: Harvard Business Publishing 8219 (2014).
  21. K Kameshwaran and K Malarvizhi. 2014. Survey on clustering techniques in data mining. International Journal of Computer Science and Information Technologies 5, 2 (2014), 2272–2276.
  22. Optimizing budget constrained spend in search advertising. In Proceedings of the sixth ACM international conference on Web search and data mining. 697–706.
  23. Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. arXiv preprint arXiv:2202.05881 (2022).
  24. Changhee Lee and Mihaela Van Der Schaar. 2020. Temporal phenotyping using deep predictive clustering of disease progression. In International Conference on Machine Learning. PMLR, 5767–5777.
  25. Duc Thanh Anh Luong and Varun Chandola. 2017. A K-Means Approach to Clustering Disease Progressions. In 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, Park City, UT, USA, August 23-26, 2017. IEEE Computer Society, 268–274. https://doi.org/10.1109/ICHI.2017.18
  26. Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features. CoRR abs/1802.01059 (2018). arXiv:1802.01059 http://arxiv.org/abs/1802.01059
  27. A novel bit level time series representation with implication of similarity search and clustering. In Pacific-Asia conference on knowledge discovery and data mining. Springer, 771–777.
  28. Unsupervised Time-Series Clustering Over Lab Data for Automatic Identification of Uncontrolled Diabetes. In 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016, Chicago, IL, USA, October 4-7, 2016. IEEE Computer Society, 72–80. https://doi.org/10.1109/ICHI.2016.14
  29. Predictive clustering. arXiv preprint arXiv:1903.08125 (2019).
  30. Nicolas E Stier-Moses. 2018. Multiplicative Pacing Equilibria in Auction Markets. In Web and Internet Economics: 14th International Conference, WINE 2018, Oxford, UK, December 15–17, 2018, Proceedings, Vol. 11316. Springer, 443.
  31. Unsupervised clickstream clustering for user behavior analysis. In Proceedings of the 2016 CHI conference on human factors in computing systems. 225–236.
  32. Joe H Ward Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American statistical association 58, 301 (1963), 236–244.
  33. Unsupervised Deep Embedding for Clustering Analysis. CoRR abs/1511.06335 (2015). arXiv:1511.06335 http://arxiv.org/abs/1511.06335
  34. Smart pacing for effective online ad campaign optimization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2217–2226.
  35. Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering. CoRR abs/1610.04794 (2016). arXiv:1610.04794 http://arxiv.org/abs/1610.04794
  36. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 1480–1489. https://doi.org/10.18653/v1/N16-1174
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

Sign Up for Free